1 Ocak 2021 Dergi makalesi Açık Erişim

Alis, Deniz; Yergin, Mert; Alis, Ceren; Topel, Cagdas; Asmakutlu, Ozan; Bagcilar, Omer; Senli, Yeseren Deniz; Ustundag, Ahmet; Salt, Vefa; Dogan, Sebahat Nacar; Velioglu, Murat; Selcuk, Hakan Hatem; Kara, Batuhan; Oksuz, Ilkay; Kizilkilic, Osman; Karaarslan, Ercan

Dublin Core

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  <dc:creator>Alis, Deniz</dc:creator>
  <dc:creator>Yergin, Mert</dc:creator>
  <dc:creator>Alis, Ceren</dc:creator>
  <dc:creator>Topel, Cagdas</dc:creator>
  <dc:creator>Asmakutlu, Ozan</dc:creator>
  <dc:creator>Bagcilar, Omer</dc:creator>
  <dc:creator>Senli, Yeseren Deniz</dc:creator>
  <dc:creator>Ustundag, Ahmet</dc:creator>
  <dc:creator>Salt, Vefa</dc:creator>
  <dc:creator>Dogan, Sebahat Nacar</dc:creator>
  <dc:creator>Velioglu, Murat</dc:creator>
  <dc:creator>Selcuk, Hakan Hatem</dc:creator>
  <dc:creator>Kara, Batuhan</dc:creator>
  <dc:creator>Oksuz, Ilkay</dc:creator>
  <dc:creator>Kizilkilic, Osman</dc:creator>
  <dc:creator>Karaarslan, Ercan</dc:creator>
  <dc:date>2021-01-01</dc:date>
  <dc:description>There is little evidence on the applicability of deep learning (DL) in the segmentation of acute ischemic lesions on diffusion-weighted imaging (DWI) between magnetic resonance imaging (MRI) scanners of different manufacturers. We retrospectively included DWI data of patients with acute ischemic lesions from six centers. Dataset A (n=2986) and B (n=3951) included data from Siemens and GE MRI scanners, respectively. The datasets were split into the training (80%), validation (10%), and internal test (10%) sets, and six neuroradiologists created ground-truth masks. Models A and B were the proposed neural networks trained on datasets A and B. The models subsequently fine-tuned across the datasets using their validation data. Another radiologist performed the segmentation on the test sets for comparisons. The median Dice scores of models A and B were 0.858 and 0.857 for the internal tests, which were non-inferior to the radiologist's performance, but demonstrated lower performance than the radiologist on the external tests. Fine-tuned models A and B achieved median Dice scores of 0.832 and 0.846, which were non-inferior to the radiologist's performance on the external tests. The present work shows that the inter-vendor operability of deep learning for the segmentation of ischemic lesions on DWI might be enhanced via transfer learning; thereby, their clinical applicability and generalizability could be improved.</dc:description>
  <dc:identifier>https://aperta.ulakbim.gov.trrecord/235848</dc:identifier>
  <dc:identifier>oai:aperta.ulakbim.gov.tr:235848</dc:identifier>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>http://www.opendefinition.org/licenses/cc-by</dc:rights>
  <dc:source>SCIENTIFIC REPORTS 11(1)</dc:source>
  <dc:title>Inter-vendor performance of deep learning in segmenting acute ischemic lesions on diffusion-weighted imaging: a multicenter study</dc:title>
  <dc:type>info:eu-repo/semantics/article</dc:type>
  <dc:type>publication-article</dc:type>
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